MODELING OF A SMALL PMSG WIND ENERGY CONVERSION SYSTEM FOR A SMART BUILDING
MODELING OF A SMALL PERMANENT MAGNET
SYNCRONO
NCRONOUS GENERATOR WIND ENERGY
ENERGY CONVERSION
SYSTEM FOR A SMART BUILDING
Eng. Vasile-Simion CRĂCIUN PhD Stud.1, Eng. Kiwoo PARK IEEE Stud. Member, PhD Stud. 2, Assoc. Prof.
Eng. Haishun SUN PhD3, Prof. Eng. Viorel TRIFA PhD1
1
Technical University of Cluj-Napoca, Department of Electrical Machines and Drives, Cluj-Napoca, Romania
2
Aalborg University, Institute of Energy Technology, Aalborg, Denmark
3
Huazhong University of Science and Technology, Department of Electrical Engineering, Wuhan, P.R. China
REZUMAT. În acestă lucrare modelarea unui generator sincron cu magneţi permanenţi (PMSG) bazat pe o turbină eoliană
va fi dezvoltată. Scopul principal al lucrării este de a combina o turbină eoliană, un PMSG, un convertor de putere şi
controlul sistemului pentru a construi un sistem complet a unei turbine eoliene pentru o clădire inteligentă. Folosind acest
model ca punct de pornire,
pornire, inginerii pot nu doar să dezvolte topologii noi pentru convertorul de putere sau a metodelor de
control care au eficienţe şi controlabilitate
controlabilitate ridicată, dar şi verificarea performanţelor lor prin simulare.
Cuvinte cheie: clădire inteligentă, generator cu magneţi permanenţi, sistem de turbină eoliană, metodă de control
ABSTRACT. In this paper, a modeling of a permanent magnet synchronous
synchronous generator (PMSG) based wind turbine system
will be developed. The main purpose of this paper is to combine a wind turbine, a PMSG, a power converter, and a control
system together to build a complete set of a wind turbine system for a residential smart building. Utilizing this model as a
starting point, engineers
engineers can not only develop new topologies of the power converter or control methods which have better
efficiency and controllability, but also verify their performance through simulation.
Keywords: smart building, permanent magnet sincronus generator, wind turbine system, control method
1. INTRODUCTION
Smart Grids and the integration of intermittent
renewable, like wind and solar power, are proposed as
costs-effective options for reducing the dependency on
fossil fuels and reducing environmental impacts from
the energy sector [1].
This development relies on successfully increasing
energy system flexibility. However, while current
research in Smart Grids and Microgrids focuses on
electro-chemical and mechanical storage options [2],
other have pointed towards options for cost-effective
integration of end-use appliances, like high-efficiency
compression heat pumps with thermal storages [3].
Over the last years, global wind energy capacity has
increased rapidly and became one of the fastest
developing renewable energy technologies. At the end
of 2006, the global wind electricity-generating capacity
has increased to 74 223 MW from 59 091 MW a year
before. The early technology used in wind turbines was
based on squirrel-cage induction generators (SCIGs)
directly connected to the grid. Recently, the technology
has developed toward variable speed. The
controllability of the wind turbines becomes more and
more important as the power level of the turbines
continuously increases [4].
In this paper, a modelling of a permanent magnet
synchronous generator (PMSG) based wind turbine
system will be developed for a Smart Building. The
main purpose of this paper is to combine a wind
turbine, a PMSG, a power converter, and a control
system together to build a complete set of a wind
turbine system for a flexible and independent energy
system.
Together with thermal storage technologies, heat
pumps, photovoltaic or solar panels, and intelligent
control system this technology may come to play a key
role in the development of Smart Buildings in future
energy systems.
The structure of the paper is: description of the
system, an overall modeling of a PMSG wind energy
conversion system (WECS), the maximum power point
tracking (MPPT) controller, simulation results, and
conclusion.
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2. SYSTEM DESCRIPTION
Wind turbine systems can be classified into three
dominant types: One is a constant speed squirrel-cage
induction generator (SCIG) based wind turbine system,
the second one is a variable speed doubly fed induction
generator (DFIG) based wind turbine system, and third
one is a variable speed permanent magnet synchronous
generator (PMSG) based wind turbine system [5].
With the development of permanent magnetic
materials in recent years, the performance of the PMSG
based wind turbine systems are improved and widely
used. This system requires neither slip rings nor an
additional power supply for the magnetic field
excitation. It can also operate in a relatively wide range
of the wind speed. Therefore, efficiency is known to be
higher than any other wind turbine systems mentioned
above.
speed ratio is constant for all maximum power points
(MPPs), while the rotational speed of the turbine is
related to the wind speed as:
ωT * =
1
V
⋅ ωr * = λopt
90
R
(2)
where ωT* is the optimal rotational speed of the
wind turbine at a certain wind speed and ωr* is the
reference rotational speed of the PMSG [7].
Fig. 2 Output power curves of the wind turbine.
Fig. 1 PMSG based wind turbine system.
Fig. 1 shows the basic concept of a PMSG based
wind turbine system. The wind turbine is connected to
the PMSG through a gearbox (1:90) and the rotational
speed of the PMSG is measured to control the speed of
the turbine. The wind speed is also measured using an
anemometer. The speed of turbine should be controlled
according to the wind speeds in order to extract the
maximum power from the wind stream. This control is
called maximum power point tracking (MPPT).
The power captured from the wind turbine can be
expressed as:
1
Pm = TmωT = πρ C p (λ , β ) R 2V 3
2
(1)
where ωT is the rotational speed of the turbine, ρ is
the air density (typically 1.25 kg/m3), Cp(λ, β) is the
power coefficient, λ is the tip-speed ratio, β is the pitch
angle, R is the blade radius, and V is the wind speed
[6].
Wind turbines can achieve their own maximum
power coefficients if they are controlled to maintain an
optimal tip-speed ratio, λopt, at various wind speeds.
Fig. 2 shows the mechanical output power of a wind
turbine at various wind speeds. The value of the tip-
The speed controller controls the speed of the PMSG
to track the reference rotational speed using a
proportional integral (PI) controller. The speed
controller produces the q-axis reference current with
which the PMSG can produce proper torque to follow
the optimal rotational speed. The d-axis reference
current is kept at zero so that no additional energy is
used for the magnetic field excitation [8].
The current controller can be implemented with
different modulation strategies such as sinusoidal pulse
width modulation (SPWM), space vector pulse width
modulation (SVPWM), and hysteresis modulation [9].
In this project, the hysteresis modulation strategy will
be used for the sake of simplicity. To control the phase
current of the PMSG, the three phase currents are
measured using current sensors. The reference dq-axis
currents are transformed into the normal three phase
currents using Park transformation and compared with
measured phase currents to perform the hysteresis
modulation. Fig. 3 shows the block diagram of the
overall control scheme.
Fig. 3 Block diagram of the overall control scheme.
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MODELING OF A SMALL PERMANENT MAGNET
MODELING
OF
A
SMALL
PMSGWIND
WINDENERGY
ENERGYCONVERSION
CONVERSIONSYSTEM
SYSTEMFOR
FORA ASMART
SMARTBUILDING
BUILDING
SYNCRONOUS GENERATOR
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3. MODEL DESCRIPTION
Overall modeling of PMSG WESC:
Fig. 4 shows the overall schematic of a Permanent
Magnet Synchronous Generator (PMSG) Wind Energy
Conversion System (WECS).
As it can be seen from the figure, a permanent
magnet synchronous machine block from the
Simulink/Sim Power Systems library is used to simulate
the PMSG. A universal bridge block of three-phase
IGBT/Diodes is used as a power converter. It is
assumed that the power converter is connected to a
520V ideal dc network which is represented by a dc
battery block in this schematic. To simulate the wind
turbine, a wind turbine block from the Simulink/Sim
Power Systems library is used. The wind turbine block
takes the wind speed in m/s, the pitch angle in degrees,
and the generator speed in per unit as inputs and
produces the mechanical torque in per unit as an output.
Two controllers are implemented to control the power
conversion system: one is the maximum power point
tracking controller and the other is the controller which
controls the speed and the phase current of the PMSG.
These controllers will be explained in detail in the next
subsection. The overall parameters for the simulation
are summarized in Table 1.
Fig. 4 Overall schematic of PMSG WESC.
Table 1
Summarized parameters for the simulation model
Blocks
Wind turbine
Parameters
10 kW
Base power of the electrical generator
10 kVA
Base wind speed
12 m/s
Maximum power at base speed
1 p.u.
Base rotational speed
1 p.u.
Pitch angle
Rated electrical power
Rated speed
Stator phase resistance
Permanent magnet
synchronous machine
Inductance Ld, Lq
Flux linkage established by magnets
Inertia
Viscous damping
pole
On resistance
IGBT/Diodes
Values
Nominal mechanical output power
0
10 kW
1800 rpm
0.2 Ω
8.5 mH
0.175 V·s
0.0089 kg·m2
0.005 N·m·s
4
1 mΩ
Snubber resistance
100 kΩ
Snubber capacitance
infinite
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MPPT Controller: Referring to (2), the optimal
rotational speed of the turbine is linearly proportional to
the speed of wind. In order to implement this relationship,
a linear function with the coefficient of 5π is used, so that
the reference speed will be 1800 rpm (60π rad/s), which is
the optimal rotational speed in this model, at the base wind
speed of 12 m/s. Furthermore, a switch is used to restrict
the reference rotational speed. When the wind speed is
lower than 4 m/s, the reference rotational speed is set to
the current rotational speed so that the turbine can
accelerate or decelerate solely by the wind and no power is
generated from the PMSG. On the other hand, when the
wind speed is higher than 4 m/s, the MPPT controller
gives the calculated optimal rotational speed as a reference
speed. Fig. 5 shows the modelling of the MPPT Control.
Fig. 5 MPPT Control
As it can be seen from Fig. 6, the speed controller is
implemented with a simple PI controller. Two zero-order
hold blocks are inserted to the inputs and a unit delay
block is inserted to the output to lower the sampling rate of
the speed controller. The sampling period of the controller
is set to 140 µs. A rate limiter block with the slew rate of
1000 is also used at the input to avoid the abrupt change in
the reference rotational speed. The rate limiter can lower
the stress in the system when the wind speed changes
abruptly. In addition, the gains in the PI controller can be
tuned more roughly. The proportional gain is set to 10 and
the integral gain is set to 300 in this model.
Fig. 6 Speed Control
Fig. 7 shows the current controller implemented in
this model. The zero-order hold blocks and unit delay
block is also used to lower the sampling rate of the
controller. The sampling period is set to 20 µs for the
current controller. The reference dq-axis currents are
first transformed into the three phase currents using
Park transformation and compared with the measured
phase currents. The error of each phase current is used
as an input to the hysteresis block. The hysteresis (∆h)
is set to 1 A in this model. When the error is bigger than
∆h, the switch is turned on to increase the phase current
and when the error is smaller than –∆h, the switch is
turned off to decrease the phase current. After
determining the switching signals in this manner for the
upper switches in the three-phase bridge converter, the
switching signals for the lower switches can be easily
obtained by complementing the upper switching
signals.
Fig. 7 Current Control
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MODELING OF A SMALL PERMANENT MAGNET
MODELING
OF
A
SMALL
PMSGWIND
WINDENERGY
ENERGYCONVERSION
CONVERSIONSYSTEM
SYSTEMFOR
FORA ASMART
SMARTBUILDING
BUILDING
SYNCRONOUS GENERATOR
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4. RESULTS
Fig. 8 shows the simulation result of the
implemented model in this project. The input wind
speed is changed from 0 to 12 m/s in a ramp manner
during first 1 s and kept constant afterwards. When the
wind speed reaches 4 m/s, the reference rotational speed
of the PMSG is set to the optimal speed to track the
maximum power points. As it can be seen from the
middle graph, the PMSG is controlled by the speed
controller and follows the reference speed very well.
The bottom graph shows the mechanical torque from
the turbine and the electromechanical torque produced
by the PMSG. The measured rotational speed of the
generator at the base wind speed is 188.5 rad/s and the
measured electromechanical torque is 53 Nm.
Therefore, neglecting the losses in the converter, the
calculated power produced by the PMSG is 9.991 kW.
Fig. 8 Simulation results when input wind speed varied from 0 to 12 m/s: wind speed (top), rotational speed (middle), and
torque (bottom).
5. CONCLUSIONS
In this project, the PMSG based wind energy
conversion system has been modelled using
Matlab/Simulink.
The basic description of each part of the system
has been given and the basic concepts of MPPT control,
speed control, and current control have been explained
briefly. The mechanical and electrical parts of the
system were modelled using the wind turbine, PMSG,
and converter blocks from the Simulink/Sim Power
Systems library.
The control methods were implemented solely
with the basic blocks from the Simulink library. The
simulation has been performed to verify the
implemented model and the simulation result showed
the reasonable speed and torque characteristics of the
controlled wind turbine system.
This model can be used to check a wind turbine
performance and behaviour at different wind speeds
with various new circuit topologies and/or control
methods.
6. AKNOWLEDGEMENT
This work was partially supported by the strategic
grant POSDRU/88/1.5/S/50783, Project ID50783
(2009), co-financed by the European Social Fund –
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Investing in People, within the Sectorial Operational
Programme Human Resources Development 2007 –
2013.
BIBLIOGRAPHY
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smart grid!. Energy building Vol. 37, 2012.
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A., Firestone R., Chandran B., Optimal Technology
Selection and Operation of Microgrids in Commercial
Buildings. IEEE 2007 PES General Meeting, 2007.
[3] Blarke, M., B., Lund H., The effectiveness of storage and
relocation options in renewable energy systems. Renewable
Energy vol. 33, 2008.
[4] Chen Z., Guerrero J., M., Blaabjerg, F., A Review of the State
of the Art of Power Electronics for Wind Turbines. IEEE
Transaction on power electronics Vol.24, 2009.
[5] Li, H., Chen, Z., Overview of different wind generator systems
and their comparisons. IET Renewable Power Generation Vol.2,
2008.
[6] Cardenas, R., Pena, T., Peres, M., Clare, J., Asher, G.,
Weeler, P., Control of a switched reluctance generator for
variable-speed wind energy applications. IEEE Transactions on
Industrial Electronics Vol.53, 2006.
[7] Koutroulis, E., Kalaitzakis, K., Design of a maximum power
tracking system for wind-energy-conversion application. IEEE
Transactions on Industrial Electronics Vol. 53, 2006.
[8] Hughes, A., Electric Motors and Drives – Fundamentals, Types and
Applications. Oxford: Elsevier, 2006.
[9] Mohan, N., Underland, T., M., Robbins, W., P., Power
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& Sons, 2003.
About the authors
Eng. Vasile-Simion CRĂCIUN, PhD Stud.
Technical University of Cluj-Napoca, Department of Electrical Machines and Drives, Cluj-Napoca, Romania
email:vasile.craciun@edr.utcluj.ro
He received the B.E. degree in Building Services and HVAC Engineering in 2009 and M.S. degree in Energetic
Management of Buildings in 2010 from Technical University of Cluj-Napoca, Romania. He has two years work
experience in HVAC as design and filed engineer and two years in micro hydro power plants as design and field engineer.
He is Ph.D. student at Technical University of Cluj-Napoca, Faculty of Electrical Engineering. He is currently involved in
research and development related to renewable energy systems, energy storage, focusing on heat pumps.
Eng. Kiwoo PARK IEEE stud. member, PhD Stud.
Aalborg University, Institute of Energy Technology, Aalborg, Denmark
email:kpa@et.aau.dk
Kiwoo Park is a PhD student at Department of Energy Technology in Aalborg University. He finished his mater degree in
Ajou University in South Korea, 2011 and he is currently working toward the Ph.D. degree at Aalborg University. His
research area is controlling switched reluctance generator and dc collection grid in a wind farm.
Assoc. Prof. Eng. Haishun SUN, PhD
Huazhong University of Science and Technology, Department of Electrical Engineering, Wuhan, P.R. China
email:Haishunsun@mail.hust.edu.cn
He received B. Eng. degree in Hydraulic machinery, M.S. and PHD degrees in electrical engineering from Huazhong
University of Science and Technology (HUST), Wuhan, China, in 1991, 1998 and 2004 respectively. He is now an
associate professor in the Department of electrical engineering at HUST. His major research interest includes power
system analysis and digital simulation, power system subsynchronous oscillation (SSO), HVDC&FACTS control, wind
power integration and energy storage and its application in power system.
Prof. Eng. Viorel TRIFA PhD
Technical University of Cluj-Napoca, Department of Electrical Machines and Drives, Cluj-Napoca, Romania
email:trifa@edr.utcluj.ro
He received the B.S. and M.S. degrees in electrical engineering from Technical University of Cluj-Napoca, Romania in
1969 and the PhD degree in electrical engineering from Polytechnic University of Timisoara, Romania in 1978. Since
1969 he has been with the Faculty of Electrical Engineering, Technical University of Cluj-Napoca, passing all academic
stages until present position as professor. He is doctorate supervisor in electrical engineering since 1991. His major
interest is electrical drives and control, reluctant motors and applications.
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